{"title":"Optimization of low-earth orbit density model based on support vector regression","authors":"Yao Wu, Junyu Chen, Chusen Lin, Zijie Li","doi":"10.1016/j.asr.2024.11.062","DOIUrl":null,"url":null,"abstract":"<div><div>With the increasing number of satellites being launched and the accumulation of space debris, the atmospheric density in low Earth orbit is becoming increasingly important for precise orbit determination. This study aims to optimize the accuracy of atmospheric density prediction in low Earth orbit using support vector regression (SVR). The SVR-based model uses high-resolution geomagnetic data and CHAMP satellite observation data to optimize the density of JB2008. Tests were conducted under various solar activity conditions and different periods. The results show that SVR improves the RMSE of the original model, the improvement rate of RMSE is between 10 % and 40 % and directly reduce MAPE to 2 %∼25 %.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"75 4","pages":"Pages 3601-3613"},"PeriodicalIF":2.8000,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Space Research","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0273117724011931","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/28 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
With the increasing number of satellites being launched and the accumulation of space debris, the atmospheric density in low Earth orbit is becoming increasingly important for precise orbit determination. This study aims to optimize the accuracy of atmospheric density prediction in low Earth orbit using support vector regression (SVR). The SVR-based model uses high-resolution geomagnetic data and CHAMP satellite observation data to optimize the density of JB2008. Tests were conducted under various solar activity conditions and different periods. The results show that SVR improves the RMSE of the original model, the improvement rate of RMSE is between 10 % and 40 % and directly reduce MAPE to 2 %∼25 %.
期刊介绍：
The COSPAR publication Advances in Space Research (ASR) is an open journal covering all areas of space research including: space studies of the Earth''s surface, meteorology, climate, the Earth-Moon system, planets and small bodies of the solar system, upper atmospheres, ionospheres and magnetospheres of the Earth and planets including reference atmospheres, space plasmas in the solar system, astrophysics from space, materials sciences in space, fundamental physics in space, space debris, space weather, Earth observations of space phenomena, etc.
NB: Please note that manuscripts related to life sciences as related to space are no more accepted for submission to Advances in Space Research. Such manuscripts should now be submitted to the new COSPAR Journal Life Sciences in Space Research (LSSR).
All submissions are reviewed by two scientists in the field. COSPAR is an interdisciplinary scientific organization concerned with the progress of space research on an international scale. Operating under the rules of ICSU, COSPAR ignores political considerations and considers all questions solely from the scientific viewpoint.
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